Overview

Dataset statistics

Number of variables13
Number of observations15148
Missing cells802
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory104.0 B

Variable types

Categorical4
Numeric9

Alerts

Day has a high cardinality: 817 distinct valuesHigh cardinality
Pfizer/BioNTech is highly overall correlated with Moderna and 2 other fieldsHigh correlation
Moderna is highly overall correlated with Pfizer/BioNTech and 2 other fieldsHigh correlation
Oxford/AstraZeneca is highly overall correlated with Pfizer/BioNTech and 2 other fieldsHigh correlation
Johnson&Johnson is highly overall correlated with Pfizer/BioNTech and 3 other fieldsHigh correlation
Sputnik V is highly overall correlated with Sinopharm/Beijing and 1 other fieldsHigh correlation
Sinopharm/Beijing is highly overall correlated with Sputnik V and 1 other fieldsHigh correlation
Novavax is highly overall correlated with ModernaHigh correlation
Covaxin is highly overall correlated with Sputnik V and 1 other fieldsHigh correlation
Entity is highly overall correlated with Oxford/AstraZeneca and 2 other fieldsHigh correlation
Code is highly overall correlated with Oxford/AstraZeneca and 2 other fieldsHigh correlation
Medicago is highly imbalanced (99.7%)Imbalance
Code has 802 (5.3%) missing valuesMissing
Moderna has 3413 (22.5%) zerosZeros
Oxford/AstraZeneca has 3006 (19.8%) zerosZeros
Johnson&Johnson has 5688 (37.5%) zerosZeros
Sputnik V has 12675 (83.7%) zerosZeros
Sinopharm/Beijing has 11209 (74.0%) zerosZeros
Novavax has 10643 (70.3%) zerosZeros
Covaxin has 13464 (88.9%) zerosZeros
Sanofi/GSK has 14941 (98.6%) zerosZeros

Reproduction

Analysis started2023-03-09 18:38:01.527985
Analysis finished2023-03-09 18:39:26.612661
Duration1 minute and 25.08 seconds
Software versionydata-profiling vv4.1.0
Download configurationconfig.json

Variables

Entity
Categorical

Distinct44
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size118.5 KiB
European Union
 
802
Italy
 
802
Germany
 
801
Argentina
 
800
Czechia
 
799
Other values (39)
11144 

Length

Max length14
Median length12
Mean length7.8009638
Min length4

Characters and Unicode

Total characters118169
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArgentina
2nd rowArgentina
3rd rowArgentina
4th rowArgentina
5th rowArgentina

Common Values

ValueCountFrequency (%)
European Union 802
 
5.3%
Italy 802
 
5.3%
Germany 801
 
5.3%
Argentina 800
 
5.3%
Czechia 799
 
5.3%
France 796
 
5.3%
Switzerland 792
 
5.2%
Peru 758
 
5.0%
South Korea 741
 
4.9%
Hong Kong 736
 
4.9%
Other values (34) 7321
48.3%

Length

2023-03-10T00:24:26.961528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
south 1012
 
5.6%
european 802
 
4.4%
union 802
 
4.4%
italy 802
 
4.4%
germany 801
 
4.4%
argentina 800
 
4.4%
czechia 799
 
4.4%
france 796
 
4.4%
switzerland 792
 
4.3%
peru 758
 
4.2%
Other values (38) 10056
55.2%

Most occurring characters

ValueCountFrequency (%)
a 15051
 
12.7%
n 11684
 
9.9%
e 9732
 
8.2%
r 8372
 
7.1%
i 7462
 
6.3%
o 6611
 
5.6%
t 6479
 
5.5%
u 5312
 
4.5%
g 3514
 
3.0%
l 3481
 
2.9%
Other values (33) 40471
34.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 96877
82.0%
Uppercase Letter 18220
 
15.4%
Space Separator 3072
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 15051
15.5%
n 11684
12.1%
e 9732
10.0%
r 8372
8.6%
i 7462
 
7.7%
o 6611
 
6.8%
t 6479
 
6.7%
u 5312
 
5.5%
g 3514
 
3.6%
l 3481
 
3.6%
Other values (14) 19179
19.8%
Uppercase Letter
ValueCountFrequency (%)
S 2774
15.2%
U 2368
13.0%
C 1686
9.3%
K 1477
8.1%
E 1282
 
7.0%
A 1183
 
6.5%
I 992
 
5.4%
P 983
 
5.4%
F 908
 
5.0%
L 895
 
4.9%
Other values (8) 3672
20.2%
Space Separator
ValueCountFrequency (%)
3072
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 115097
97.4%
Common 3072
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 15051
13.1%
n 11684
 
10.2%
e 9732
 
8.5%
r 8372
 
7.3%
i 7462
 
6.5%
o 6611
 
5.7%
t 6479
 
5.6%
u 5312
 
4.6%
g 3514
 
3.1%
l 3481
 
3.0%
Other values (32) 37399
32.5%
Common
ValueCountFrequency (%)
3072
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118169
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 15051
 
12.7%
n 11684
 
9.9%
e 9732
 
8.2%
r 8372
 
7.1%
i 7462
 
6.3%
o 6611
 
5.6%
t 6479
 
5.5%
u 5312
 
4.5%
g 3514
 
3.0%
l 3481
 
2.9%
Other values (33) 40471
34.2%

Code
Categorical

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)0.3%
Missing802
Missing (%)5.3%
Memory size118.5 KiB
ITA
 
802
DEU
 
801
ARG
 
800
CZE
 
799
FRA
 
796
Other values (38)
10348 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters43038
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowARG
2nd rowARG
3rd rowARG
4th rowARG
5th rowARG

Common Values

ValueCountFrequency (%)
ITA 802
 
5.3%
DEU 801
 
5.3%
ARG 800
 
5.3%
CZE 799
 
5.3%
FRA 796
 
5.3%
CHE 792
 
5.2%
PER 758
 
5.0%
KOR 741
 
4.9%
HKG 736
 
4.9%
JPN 732
 
4.8%
Other values (33) 6589
43.5%
(Missing) 802
 
5.3%

Length

2023-03-10T00:24:27.497174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ita 802
 
5.6%
deu 801
 
5.6%
arg 800
 
5.6%
cze 799
 
5.6%
fra 796
 
5.5%
che 792
 
5.5%
per 758
 
5.3%
kor 741
 
5.2%
hkg 736
 
5.1%
jpn 732
 
5.1%
Other values (33) 6589
45.9%

Most occurring characters

ValueCountFrequency (%)
R 5217
12.1%
E 4058
 
9.4%
A 3956
 
9.2%
U 3717
 
8.6%
C 2751
 
6.4%
H 2336
 
5.4%
L 2219
 
5.2%
K 2069
 
4.8%
P 2048
 
4.8%
G 1649
 
3.8%
Other values (15) 13018
30.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 43038
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 5217
12.1%
E 4058
 
9.4%
A 3956
 
9.2%
U 3717
 
8.6%
C 2751
 
6.4%
H 2336
 
5.4%
L 2219
 
5.2%
K 2069
 
4.8%
P 2048
 
4.8%
G 1649
 
3.8%
Other values (15) 13018
30.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 43038
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 5217
12.1%
E 4058
 
9.4%
A 3956
 
9.2%
U 3717
 
8.6%
C 2751
 
6.4%
H 2336
 
5.4%
L 2219
 
5.2%
K 2069
 
4.8%
P 2048
 
4.8%
G 1649
 
3.8%
Other values (15) 13018
30.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 5217
12.1%
E 4058
 
9.4%
A 3956
 
9.2%
U 3717
 
8.6%
C 2751
 
6.4%
H 2336
 
5.4%
L 2219
 
5.2%
K 2069
 
4.8%
P 2048
 
4.8%
G 1649
 
3.8%
Other values (15) 13018
30.2%

Day
Categorical

Distinct817
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size118.5 KiB
03/04/22
 
41
01/28/22
 
41
01/14/22
 
41
01/21/22
 
41
03/11/22
 
41
Other values (812)
14943 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters121184
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st row12/29/20
2nd row12/30/20
3rd row12/31/20
4th row01/01/21
5th row01/02/21

Common Values

ValueCountFrequency (%)
03/04/22 41
 
0.3%
01/28/22 41
 
0.3%
01/14/22 41
 
0.3%
01/21/22 41
 
0.3%
03/11/22 41
 
0.3%
03/25/22 41
 
0.3%
01/07/22 41
 
0.3%
03/19/21 40
 
0.3%
02/25/22 40
 
0.3%
12/10/21 40
 
0.3%
Other values (807) 14741
97.3%

Length

2023-03-10T00:24:27.968079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
03/04/22 41
 
0.3%
01/14/22 41
 
0.3%
01/21/22 41
 
0.3%
03/11/22 41
 
0.3%
03/25/22 41
 
0.3%
01/07/22 41
 
0.3%
01/28/22 41
 
0.3%
04/16/21 40
 
0.3%
07/02/21 40
 
0.3%
08/13/21 40
 
0.3%
Other values (807) 14741
97.3%

Most occurring characters

ValueCountFrequency (%)
2 31032
25.6%
/ 30296
25.0%
1 20236
16.7%
0 18869
15.6%
3 4514
 
3.7%
5 2801
 
2.3%
4 2770
 
2.3%
6 2713
 
2.2%
7 2697
 
2.2%
8 2665
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 90888
75.0%
Other Punctuation 30296
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 31032
34.1%
1 20236
22.3%
0 18869
20.8%
3 4514
 
5.0%
5 2801
 
3.1%
4 2770
 
3.0%
6 2713
 
3.0%
7 2697
 
3.0%
8 2665
 
2.9%
9 2591
 
2.9%
Other Punctuation
ValueCountFrequency (%)
/ 30296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 121184
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 31032
25.6%
/ 30296
25.0%
1 20236
16.7%
0 18869
15.6%
3 4514
 
3.7%
5 2801
 
2.3%
4 2770
 
2.3%
6 2713
 
2.2%
7 2697
 
2.2%
8 2665
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121184
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 31032
25.6%
/ 30296
25.0%
1 20236
16.7%
0 18869
15.6%
3 4514
 
3.7%
5 2801
 
2.3%
4 2770
 
2.3%
6 2713
 
2.2%
7 2697
 
2.2%
8 2665
 
2.2%

Pfizer/BioNTech
Real number (ℝ)

Distinct14726
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60731824
Minimum0
Maximum6.6514172 × 108
Zeros96
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size118.5 KiB
2023-03-10T00:24:28.576948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35136.45
Q12390724.5
median10291372
Q352235442
95-th percentile3.1626803 × 108
Maximum6.6514172 × 108
Range6.6514172 × 108
Interquartile range (IQR)49844717

Descriptive statistics

Standard deviation1.2390971 × 108
Coefficient of variation (CV)2.0402764
Kurtosis11.119043
Mean60731824
Median Absolute Deviation (MAD)9577445
Skewness3.2670255
Sum9.1996568 × 1011
Variance1.5353616 × 1016
MonotonicityNot monotonic
2023-03-10T00:24:29.182208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 96
 
0.6%
16 28
 
0.2%
19 18
 
0.1%
619997 16
 
0.1%
58 13
 
0.1%
40 12
 
0.1%
20 12
 
0.1%
151325 11
 
0.1%
616757 10
 
0.1%
73 9
 
0.1%
Other values (14716) 14923
98.5%
ValueCountFrequency (%)
0 96
0.6%
1 3
 
< 0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
6 3
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
665141725 1
< 0.1%
665126790 1
< 0.1%
665113928 1
< 0.1%
665096645 1
< 0.1%
665085937 1
< 0.1%
665085434 1
< 0.1%
665078911 1
< 0.1%
664854729 1
< 0.1%
664833850 1
< 0.1%
664816492 1
< 0.1%

Moderna
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11116
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17244077
Minimum0
Maximum2.5141288 × 108
Zeros3413
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size118.5 KiB
2023-03-10T00:24:29.880337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1729
median1321525.5
Q313345395
95-th percentile1.3506022 × 108
Maximum2.5141288 × 108
Range2.5141288 × 108
Interquartile range (IQR)13344666

Descriptive statistics

Standard deviation39589144
Coefficient of variation (CV)2.2958111
Kurtosis11.002661
Mean17244077
Median Absolute Deviation (MAD)1321525.5
Skewness3.3079865
Sum2.6121328 × 1011
Variance1.5673003 × 1015
MonotonicityNot monotonic
2023-03-10T00:24:30.705870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3413
 
22.5%
2133932 55
 
0.4%
13433166 39
 
0.3%
1 35
 
0.2%
12 32
 
0.2%
4 30
 
0.2%
3 24
 
0.2%
6493 24
 
0.2%
10 21
 
0.1%
13433163 17
 
0.1%
Other values (11106) 11458
75.6%
ValueCountFrequency (%)
0 3413
22.5%
1 35
 
0.2%
2 15
 
0.1%
3 24
 
0.2%
4 30
 
0.2%
5 3
 
< 0.1%
6 12
 
0.1%
7 12
 
0.1%
8 11
 
0.1%
9 13
 
0.1%
ValueCountFrequency (%)
251412884 1
< 0.1%
251252512 1
< 0.1%
251049942 1
< 0.1%
250820655 1
< 0.1%
250581571 1
< 0.1%
250310595 1
< 0.1%
249955722 1
< 0.1%
249495031 1
< 0.1%
248998772 1
< 0.1%
248567365 1
< 0.1%

Oxford/AstraZeneca
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7770
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6888349.8
Minimum0
Maximum67196830
Zeros3006
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size118.5 KiB
2023-03-10T00:24:31.387411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q149273.75
median722953
Q37952160
95-th percentile26776775
Maximum67196830
Range67196830
Interquartile range (IQR)7902886.2

Descriptive statistics

Standard deviation14214985
Coefficient of variation (CV)2.0636271
Kurtosis10.299873
Mean6888349.8
Median Absolute Deviation (MAD)722953
Skewness3.1854603
Sum1.0434472 × 1011
Variance2.0206581 × 1014
MonotonicityNot monotonic
2023-03-10T00:24:32.148975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3006
 
19.8%
12173153 240
 
1.6%
886784 166
 
1.1%
117824 159
 
1.0%
7862984 132
 
0.9%
549673 123
 
0.8%
262043 117
 
0.8%
1 114
 
0.8%
20071347 110
 
0.7%
849559 107
 
0.7%
Other values (7760) 10874
71.8%
ValueCountFrequency (%)
0 3006
19.8%
1 114
 
0.8%
2 30
 
0.2%
3 18
 
0.1%
4 21
 
0.1%
5 11
 
0.1%
6 5
 
< 0.1%
7 15
 
0.1%
8 12
 
0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
67196830 13
0.1%
67196829 7
< 0.1%
67196827 7
< 0.1%
67196824 7
< 0.1%
67196819 4
 
< 0.1%
67196818 3
 
< 0.1%
67196809 2
 
< 0.1%
67196808 5
 
< 0.1%
67196795 7
< 0.1%
67196787 7
< 0.1%

Johnson&Johnson
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7521
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1788717.2
Minimum0
Maximum18982882
Zeros5688
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size118.5 KiB
2023-03-10T00:24:33.097500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31008
Q31164479
95-th percentile15398569
Maximum18982882
Range18982882
Interquartile range (IQR)1164479

Descriptive statistics

Standard deviation4381771.9
Coefficient of variation (CV)2.4496728
Kurtosis8.1828148
Mean1788717.2
Median Absolute Deviation (MAD)31008
Skewness3.0452357
Sum2.7095488 × 1010
Variance1.9199925 × 1013
MonotonicityNot monotonic
2023-03-10T00:24:33.910110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5688
37.5%
20680 158
 
1.0%
1 82
 
0.5%
2 57
 
0.4%
18 45
 
0.3%
1508417 45
 
0.3%
390 39
 
0.3%
16 36
 
0.2%
1508330 34
 
0.2%
12 33
 
0.2%
Other values (7511) 8931
59.0%
ValueCountFrequency (%)
0 5688
37.5%
1 82
 
0.5%
2 57
 
0.4%
3 25
 
0.2%
4 30
 
0.2%
5 28
 
0.2%
6 27
 
0.2%
7 16
 
0.1%
8 4
 
< 0.1%
9 14
 
0.1%
ValueCountFrequency (%)
18982882 1
< 0.1%
18979373 1
< 0.1%
18976061 1
< 0.1%
18971123 1
< 0.1%
18967308 1
< 0.1%
18963745 1
< 0.1%
18960250 1
< 0.1%
18956612 1
< 0.1%
18954913 1
< 0.1%
18952708 1
< 0.1%

Sputnik V
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct844
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean901190.13
Minimum0
Maximum20755225
Zeros12675
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size118.5 KiB
2023-03-10T00:24:34.731901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1845386
Maximum20755225
Range20755225
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3789409.2
Coefficient of variation (CV)4.2048942
Kurtosis20.235904
Mean901190.13
Median Absolute Deviation (MAD)0
Skewness4.6340334
Sum1.3651228 × 1010
Variance1.4359622 × 1013
MonotonicityNot monotonic
2023-03-10T00:24:35.377775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12675
83.7%
10 488
 
3.2%
1845386 419
 
2.8%
8 125
 
0.8%
1807392 66
 
0.4%
37984 59
 
0.4%
1845360 35
 
0.2%
9 29
 
0.2%
1845368 21
 
0.1%
37958 16
 
0.1%
Other values (834) 1215
 
8.0%
ValueCountFrequency (%)
0 12675
83.7%
2 15
 
0.1%
3 11
 
0.1%
4 12
 
0.1%
5 8
 
0.1%
6 16
 
0.1%
7 1
 
< 0.1%
8 125
 
0.8%
9 29
 
0.2%
10 488
 
3.2%
ValueCountFrequency (%)
20755225 2
 
< 0.1%
20755206 1
 
< 0.1%
20755200 4
< 0.1%
20755198 1
 
< 0.1%
20755192 2
 
< 0.1%
20755191 3
 
< 0.1%
20755190 1
 
< 0.1%
20755189 6
< 0.1%
20755188 2
 
< 0.1%
20755187 8
0.1%

Sinopharm/Beijing
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2064
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015890.7
Minimum0
Maximum28951432
Zeros11209
Zeros (%)74.0%
Negative0
Negative (%)0.0%
Memory size118.5 KiB
2023-03-10T00:24:36.143431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile20902861
Maximum28951432
Range28951432
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6339738.9
Coefficient of variation (CV)3.1448823
Kurtosis9.3083941
Mean2015890.7
Median Absolute Deviation (MAD)0
Skewness3.26281
Sum3.0536712 × 1010
Variance4.019229 × 1013
MonotonicityNot monotonic
2023-03-10T00:24:37.128511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11209
74.0%
27 328
 
2.2%
77 153
 
1.0%
72 134
 
0.9%
76 57
 
0.4%
2 36
 
0.2%
21 32
 
0.2%
66 30
 
0.2%
64 28
 
0.2%
7 26
 
0.2%
Other values (2054) 3115
 
20.6%
ValueCountFrequency (%)
0 11209
74.0%
1 17
 
0.1%
2 36
 
0.2%
3 13
 
0.1%
4 9
 
0.1%
5 25
 
0.2%
6 11
 
0.1%
7 26
 
0.2%
8 8
 
0.1%
9 11
 
0.1%
ValueCountFrequency (%)
28951432 2
< 0.1%
28951287 1
< 0.1%
28951124 1
< 0.1%
28951117 1
< 0.1%
28951084 1
< 0.1%
28950872 1
< 0.1%
28950632 1
< 0.1%
28950382 1
< 0.1%
28950056 1
< 0.1%
28949636 1
< 0.1%

Novavax
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3023
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20603.826
Minimum0
Maximum305631
Zeros10643
Zeros (%)70.3%
Negative0
Negative (%)0.0%
Memory size118.5 KiB
2023-03-10T00:24:37.943201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3100
95-th percentile208122.6
Maximum305631
Range305631
Interquartile range (IQR)100

Descriptive statistics

Standard deviation62036.47
Coefficient of variation (CV)3.0109199
Kurtosis9.6427054
Mean20603.826
Median Absolute Deviation (MAD)0
Skewness3.2729786
Sum3.1210676 × 108
Variance3.8485236 × 109
MonotonicityNot monotonic
2023-03-10T00:24:38.962146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10643
70.3%
42938 90
 
0.6%
1 45
 
0.3%
57 43
 
0.3%
11113 41
 
0.3%
2 36
 
0.2%
14 31
 
0.2%
6 31
 
0.2%
16 27
 
0.2%
3 24
 
0.2%
Other values (3013) 4137
 
27.3%
ValueCountFrequency (%)
0 10643
70.3%
1 45
 
0.3%
2 36
 
0.2%
3 24
 
0.2%
4 6
 
< 0.1%
5 8
 
0.1%
6 31
 
0.2%
7 12
 
0.1%
8 5
 
< 0.1%
9 19
 
0.1%
ValueCountFrequency (%)
305631 1
 
< 0.1%
305483 1
 
< 0.1%
305298 1
 
< 0.1%
305105 1
 
< 0.1%
304726 1
 
< 0.1%
304315 1
 
< 0.1%
304144 3
< 0.1%
304139 2
< 0.1%
304135 1
 
< 0.1%
304128 1
 
< 0.1%

Covaxin
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0250198
Minimum0
Maximum132
Zeros13464
Zeros (%)88.9%
Negative0
Negative (%)0.0%
Memory size118.5 KiB
2023-03-10T00:24:39.859370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25.65
Maximum132
Range132
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.456886
Coefficient of variation (CV)4.2251954
Kurtosis17.136386
Mean6.0250198
Median Absolute Deviation (MAD)0
Skewness4.3304473
Sum91267
Variance648.05305
MonotonicityNot monotonic
2023-03-10T00:24:40.717262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13464
88.9%
5 445
 
2.9%
4 165
 
1.1%
132 160
 
1.1%
2 145
 
1.0%
127 99
 
0.7%
128 56
 
0.4%
126 38
 
0.3%
3 33
 
0.2%
122 32
 
0.2%
Other values (67) 511
 
3.4%
ValueCountFrequency (%)
0 13464
88.9%
1 21
 
0.1%
2 145
 
1.0%
3 33
 
0.2%
4 165
 
1.1%
5 445
 
2.9%
6 22
 
0.1%
7 1
 
< 0.1%
8 9
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
132 160
1.1%
131 21
 
0.1%
128 56
 
0.4%
127 99
0.7%
126 38
 
0.3%
125 14
 
0.1%
124 28
 
0.2%
123 15
 
0.1%
122 32
 
0.2%
121 5
 
< 0.1%

Medicago
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.5 KiB
0
15140 
863
 
5
285
 
1
518
 
1
580
 
1

Length

Max length3
Median length1
Mean length1.0010562
Min length1

Characters and Unicode

Total characters15164
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15140
99.9%
863 5
 
< 0.1%
285 1
 
< 0.1%
518 1
 
< 0.1%
580 1
 
< 0.1%

Length

2023-03-10T00:24:41.599752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T00:24:42.476317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 15140
99.9%
863 5
 
< 0.1%
285 1
 
< 0.1%
518 1
 
< 0.1%
580 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15141
99.8%
8 8
 
0.1%
6 5
 
< 0.1%
3 5
 
< 0.1%
5 3
 
< 0.1%
2 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15164
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15141
99.8%
8 8
 
0.1%
6 5
 
< 0.1%
3 5
 
< 0.1%
5 3
 
< 0.1%
2 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15164
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15141
99.8%
8 8
 
0.1%
6 5
 
< 0.1%
3 5
 
< 0.1%
5 3
 
< 0.1%
2 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15164
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15141
99.8%
8 8
 
0.1%
6 5
 
< 0.1%
3 5
 
< 0.1%
5 3
 
< 0.1%
2 1
 
< 0.1%
1 1
 
< 0.1%

Sanofi/GSK
Real number (ℝ)

Distinct159
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.102324
Minimum0
Maximum4237
Zeros14941
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size118.5 KiB
2023-03-10T00:24:43.142834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4237
Range4237
Interquartile range (IQR)0

Descriptive statistics

Standard deviation271.56165
Coefficient of variation (CV)11.754733
Kurtosis169.5545
Mean23.102324
Median Absolute Deviation (MAD)0
Skewness12.84101
Sum349954
Variance73745.732
MonotonicityNot monotonic
2023-03-10T00:24:44.296596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14941
98.6%
88 14
 
0.1%
104 7
 
< 0.1%
4237 7
 
< 0.1%
10 6
 
< 0.1%
92 4
 
< 0.1%
32 3
 
< 0.1%
26 3
 
< 0.1%
3759 2
 
< 0.1%
95 2
 
< 0.1%
Other values (149) 159
 
1.0%
ValueCountFrequency (%)
0 14941
98.6%
1 2
 
< 0.1%
2 2
 
< 0.1%
10 6
 
< 0.1%
17 1
 
< 0.1%
18 1
 
< 0.1%
19 2
 
< 0.1%
20 2
 
< 0.1%
26 3
 
< 0.1%
28 1
 
< 0.1%
ValueCountFrequency (%)
4237 7
< 0.1%
4202 1
 
< 0.1%
4184 1
 
< 0.1%
4166 1
 
< 0.1%
4138 2
 
< 0.1%
4133 1
 
< 0.1%
4090 1
 
< 0.1%
4038 1
 
< 0.1%
4020 1
 
< 0.1%
3976 1
 
< 0.1%

Interactions

2023-03-10T00:24:17.734726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:13.620795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:19.331753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:25.144286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:33.214040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:40.589764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:55.359138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:04.040576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:11.609912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:18.419044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:14.405988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:20.007585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:25.816089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:34.047253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:41.553960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:56.820388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:04.918074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:12.224564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:19.066674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:15.008163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:20.677648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:26.487655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:34.927958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:42.406496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:58.101983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:05.679648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:12.792328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:19.919433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:15.603253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:21.550147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:27.365772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:35.791049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:45.631093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:58.869545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:06.537407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:13.664592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:20.639791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:16.225893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:22.208769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:28.415789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:36.812140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:47.410717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:59.834986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:07.589805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:14.302751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:21.332808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:16.795901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:22.789243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:29.426588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:37.431789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:48.656119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:00.822422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:08.486681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:14.896357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:22.047438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:17.466172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:23.470852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:30.132418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:38.100407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:50.209751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:01.691922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:09.516067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:15.684770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:22.673226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:18.106807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:24.062631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:31.251584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:38.854082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:51.679830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:02.367535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:10.256678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:16.420405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:23.287681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:18.723064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:24.626451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:32.049601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:39.746266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:23:53.469832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:03.294004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:11.018250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-10T00:24:17.054111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-10T00:24:44.985689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Pfizer/BioNTechModernaOxford/AstraZenecaJohnson&JohnsonSputnik VSinopharm/BeijingNovavaxCovaxinSanofi/GSKEntityCodeMedicago
Pfizer/BioNTech1.0000.7720.5010.5400.1340.0590.4780.2480.1580.4710.4920.000
Moderna0.7721.0000.4940.6030.2160.0980.5300.2190.1540.4370.4280.007
Oxford/AstraZeneca0.5010.4941.0000.4050.4130.4070.4060.2780.1420.5480.7970.000
Johnson&Johnson0.5400.6030.4051.0000.082-0.0320.4770.3090.1440.5090.5110.000
Sputnik V0.1340.2160.4130.0821.0000.7160.2440.6340.0730.3100.3100.000
Sinopharm/Beijing0.0590.0980.407-0.0320.7161.0000.1110.5150.0460.4400.4390.000
Novavax0.4780.5300.4060.4770.2440.1111.0000.4580.2160.3470.3460.000
Covaxin0.2480.2190.2780.3090.6340.5150.4581.0000.1260.3080.3070.000
Sanofi/GSK0.1580.1540.1420.1440.0730.0460.2160.1261.0000.0750.0690.000
Entity0.4710.4370.5480.5090.3100.4400.3470.3080.0751.0001.0000.150
Code0.4920.4280.7970.5110.3100.4390.3460.3070.0691.0001.0000.149
Medicago0.0000.0070.0000.0000.0000.0000.0000.0000.0000.1500.1491.000

Missing values

2023-03-10T00:24:24.522984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-10T00:24:25.907302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EntityCodeDayPfizer/BioNTechModernaOxford/AstraZenecaJohnson&JohnsonSputnik VSinopharm/BeijingNovavaxCovaxinMedicagoSanofi/GSK
0ArgentinaARG12/29/2000102049110000
1ArgentinaARG12/30/2000104059210000
2ArgentinaARG12/31/2000104339810000
3ArgentinaARG01/01/2100304352510000
4ArgentinaARG01/02/2110404683510000
5ArgentinaARG01/03/2130404727910000
6ArgentinaARG01/04/2130405774010000
7ArgentinaARG01/05/2130406846650000
8ArgentinaARG01/06/2160507857980000
9ArgentinaARG01/07/21809096804100000
EntityCodeDayPfizer/BioNTechModernaOxford/AstraZenecaJohnson&JohnsonSputnik VSinopharm/BeijingNovavaxCovaxinMedicagoSanofi/GSK
15138UruguayURY02/21/2325657780912000000000
15139UruguayURY02/24/2325658990912000000000
15140UruguayURY02/25/2325659720912000000000
15141UruguayURY02/26/2325660400912000000000
15142UruguayURY02/27/2325661720912000000000
15143UruguayURY02/28/2325662470912000000000
15144UruguayURY03/01/2325662480912000000000
15145UruguayURY03/03/2325663180912000000000
15146UruguayURY03/04/2325663900912000000000
15147UruguayURY03/05/2325664320912000000000